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alpha-mind
Commit 48fc6a88 authored by Dr.李's avatar Dr.李
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update strategy

parent cfdc9444
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Showing with 108 additions and 99 deletions
alphamind/strategy/strategy.py
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...@@ -30,31 +30,16 @@ all_styles = risk_styles + industry_styles + macro_styles ...@@ -30,31 +30,16 @@ all_styles = risk_styles + industry_styles + macro_styles
class RunningSetting(object): class RunningSetting(object):
def __init__(self, def __init__(self,
universe,
start_date,
end_date,
freq,
benchmark=905,
lbound=None, lbound=None,
ubound=None, ubound=None,
weights_bandwidth=None, weights_bandwidth=None,
industry_cat='sw_adj',
industry_level=1,
rebalance_method='risk_neutral', rebalance_method='risk_neutral',
bounds=None, bounds=None,
**kwargs): **kwargs):
self.universe = universe
self.dates = makeSchedule(start_date, end_date, freq, 'china.sse')
self.dates = [d.strftime('%Y-%m-%d') for d in self.dates]
self.benchmark = benchmark
self.lbound = lbound self.lbound = lbound
self.ubound = ubound self.ubound = ubound
self.weights_bandwidth = weights_bandwidth self.weights_bandwidth = weights_bandwidth
self.freq = freq
self.horizon = map_freq(freq)
self.executor = NaiveExecutor() self.executor = NaiveExecutor()
self.industry_cat = industry_cat
self.industry_level = industry_level
self.rebalance_method = rebalance_method self.rebalance_method = rebalance_method
self.bounds = bounds self.bounds = bounds
self.more_opts = kwargs self.more_opts = kwargs
...@@ -65,24 +50,83 @@ class Strategy(object): ...@@ -65,24 +50,83 @@ class Strategy(object):
def __init__(self, def __init__(self,
alpha_model, alpha_model,
data_meta, data_meta,
running_setting, universe,
start_date,
end_date,
freq,
benchmark=905,
industry_cat='sw_adj',
industry_level=1,
dask_client=None): dask_client=None):
self.alpha_model = alpha_model self.alpha_model = alpha_model
self.data_meta = data_meta self.data_meta = data_meta
self.running_setting = running_setting self.universe = universe
self.benchmark = benchmark
self.dates = makeSchedule(start_date, end_date, freq, 'china.sse')
self.dates = [d.strftime('%Y-%m-%d') for d in self.dates]
self.industry_cat = industry_cat
self.industry_level = industry_level
self.freq = freq
self.horizon = map_freq(freq)
self.engine = SqlEngine(self.data_meta.data_source) self.engine = SqlEngine(self.data_meta.data_source)
self.dask_client = dask_client self.dask_client = dask_client
def _create_lu_bounds(self, codes, benchmark_w): def prepare_backtest_data(self):
total_factors = self.engine.fetch_factor_range(self.universe,
self.alpha_model.formulas,
dates=self.dates)
alpha_logger.info("alpha factor data loading finished ...")
total_industry = self.engine.fetch_industry_matrix_range(self.universe,
dates=self.dates,
category=self.industry_cat,
level=self.industry_level)
alpha_logger.info("industry data loading finished ...")
total_benchmark = self.engine.fetch_benchmark_range(dates=self.dates,
benchmark=self.benchmark)
alpha_logger.info("benchmark data loading finished ...")
total_risk_cov, total_risk_exposure = self.engine.fetch_risk_model_range(
self.universe,
dates=self.dates,
risk_model=self.data_meta.risk_model
)
alpha_logger.info("risk_model data loading finished ...")
total_returns = self.engine.fetch_dx_return_range(self.universe,
dates=self.dates,
horizon=self.horizon,
offset=1)
alpha_logger.info("returns data loading finished ...")
total_data = pd.merge(total_factors, total_industry, on=['trade_date', 'code'])
total_data = pd.merge(total_data, total_benchmark, on=['trade_date', 'code'], how='left')
total_data.fillna({'weight': 0.}, inplace=True)
total_data = pd.merge(total_data, total_returns, on=['trade_date', 'code'])
total_data = pd.merge(total_data, total_risk_exposure, on=['trade_date', 'code'])
is_in_benchmark = (total_data.weight > 0.).astype(float).values.reshape((-1, 1))
total_data.loc[:, 'benchmark'] = is_in_benchmark
total_data.loc[:, 'total'] = np.ones_like(is_in_benchmark)
total_data.sort_values(['trade_date', 'code'], inplace=True)
self.index_return = self.engine.fetch_dx_return_index_range(self.benchmark,
dates=self.dates,
horizon=self.horizon,
offset=1).set_index('trade_date')
self.total_data = total_data
self.total_risk_cov = total_risk_cov
def _create_lu_bounds(self, running_setting, codes, benchmark_w):
codes = np.array(codes) codes = np.array(codes)
if self.running_setting.weights_bandwidth: if running_setting.weights_bandwidth:
lbound = np.maximum(0., benchmark_w - self.running_setting.weights_bandwidth) lbound = np.maximum(0., benchmark_w - running_setting.weights_bandwidth)
ubound = self.running_setting.weights_bandwidth + benchmark_w ubound = running_setting.weights_bandwidth + benchmark_w
lb = self.running_setting.lbound lb = running_setting.lbound
ub = self.running_setting.ubound ub = running_setting.ubound
if lb or ub: if lb or ub:
if not isinstance(lb, dict): if not isinstance(lb, dict):
...@@ -109,59 +153,20 @@ class Strategy(object): ...@@ -109,59 +153,20 @@ class Strategy(object):
ubound[i] = ub['other'] ubound[i] = ub['other']
return lbound, ubound return lbound, ubound
def run(self): def run(self, running_setting):
alpha_logger.info("starting backting ...") alpha_logger.info("starting backting ...")
total_data_groups = self.total_data.groupby('trade_date')
total_factors = self.engine.fetch_factor_range(self.running_setting.universe,
self.alpha_model.formulas,
dates=self.running_setting.dates)
alpha_logger.info("alpha factor data loading finished ...")
total_industry = self.engine.fetch_industry_matrix_range(self.running_setting.universe,
dates=self.running_setting.dates,
category=self.running_setting.industry_cat,
level=self.running_setting.industry_level)
alpha_logger.info("industry data loading finished ...")
total_benchmark = self.engine.fetch_benchmark_range(dates=self.running_setting.dates,
benchmark=self.running_setting.benchmark)
alpha_logger.info("benchmark data loading finished ...")
total_risk_cov, total_risk_exposure = self.engine.fetch_risk_model_range(
self.running_setting.universe,
dates=self.running_setting.dates,
risk_model=self.data_meta.risk_model
)
alpha_logger.info("risk_model data loading finished ...")
total_returns = self.engine.fetch_dx_return_range(self.running_setting.universe,
dates=self.running_setting.dates,
horizon=self.running_setting.horizon,
offset=1)
alpha_logger.info("returns data loading finished ...")
total_data = pd.merge(total_factors, total_industry, on=['trade_date', 'code'])
total_data = pd.merge(total_data, total_benchmark, on=['trade_date', 'code'], how='left')
total_data.fillna({'weight': 0.}, inplace=True)
total_data = pd.merge(total_data, total_returns, on=['trade_date', 'code'])
total_data = pd.merge(total_data, total_risk_exposure, on=['trade_date', 'code'])
is_in_benchmark = (total_data.weight > 0.).astype(float).values.reshape((-1, 1))
total_data.loc[:, 'benchmark'] = is_in_benchmark
total_data.loc[:, 'total'] = np.ones_like(is_in_benchmark)
total_data.sort_values(['trade_date', 'code'], inplace=True)
total_data_groups = total_data.groupby('trade_date')
rets = [] rets = []
turn_overs = [] turn_overs = []
leverags = [] leverags = []
previous_pos = pd.DataFrame() previous_pos = pd.DataFrame()
executor = copy.deepcopy(self.running_setting.executor) executor = copy.deepcopy(running_setting.executor)
positions = pd.DataFrame() positions = pd.DataFrame()
if self.dask_client is None: if self.dask_client is None:
models = {} models = {}
for ref_date, _ in total_data_groups: for ref_date, _ in self.total_data_groups:
models[ref_date] = train_model(ref_date.strftime('%Y-%m-%d'), self.alpha_model, self.data_meta) models[ref_date] = train_model(ref_date.strftime('%Y-%m-%d'), self.alpha_model, self.data_meta)
else: else:
def worker(parameters): def worker(parameters):
...@@ -185,18 +190,18 @@ class Strategy(object): ...@@ -185,18 +190,18 @@ class Strategy(object):
remained_pos.fillna(0., inplace=True) remained_pos.fillna(0., inplace=True)
current_position = remained_pos.weight.values current_position = remained_pos.weight.values
if self.running_setting.rebalance_method == 'tv': if running_setting.rebalance_method == 'tv':
risk_cov = total_risk_cov[total_risk_cov.trade_date == ref_date] risk_cov = self.total_risk_cov[self.total_risk_cov.trade_date == ref_date]
sec_cov = self._generate_sec_cov(this_data, risk_cov) sec_cov = self._generate_sec_cov(this_data, risk_cov)
else: else:
sec_cov = None sec_cov = None
benchmark_w = this_data.weight.values benchmark_w = this_data.weight.values
constraints = LinearConstraints(self.running_setting.bounds, constraints = LinearConstraints(running_setting.bounds,
this_data, this_data,
benchmark_w) benchmark_w)
lbound, ubound = self._create_lu_bounds(codes, benchmark_w) lbound, ubound = self._create_lu_bounds(running_setting, codes, benchmark_w)
features = new_model.features features = new_model.features
dfs = [] dfs = []
...@@ -205,7 +210,9 @@ class Strategy(object): ...@@ -205,7 +210,9 @@ class Strategy(object):
raw_factors = data_cleaned[[name]].values raw_factors = data_cleaned[[name]].values
new_factors = factor_processing(raw_factors, new_factors = factor_processing(raw_factors,
pre_process=self.data_meta.pre_process, pre_process=self.data_meta.pre_process,
risk_factors=data_cleaned[self.data_meta.neutralized_risk].values.astype(float) if self.data_meta.neutralized_risk else None, risk_factors=data_cleaned[
self.data_meta.neutralized_risk].values.astype(
float) if self.data_meta.neutralized_risk else None,
post_process=self.data_meta.post_process) post_process=self.data_meta.post_process)
df = pd.DataFrame(new_factors, columns=[name], index=data_cleaned.code) df = pd.DataFrame(new_factors, columns=[name], index=data_cleaned.code)
dfs.append(df) dfs.append(df)
...@@ -215,15 +222,15 @@ class Strategy(object): ...@@ -215,15 +222,15 @@ class Strategy(object):
er = new_model.predict(new_factors).astype(float) er = new_model.predict(new_factors).astype(float)
alpha_logger.info('{0} re-balance: {1} codes'.format(ref_date, len(er))) alpha_logger.info('{0} re-balance: {1} codes'.format(ref_date, len(er)))
target_pos = self._calculate_pos(er, target_pos = self._calculate_pos(running_setting,
er,
this_data, this_data,
constraints, constraints,
benchmark_w, benchmark_w,
lbound, lbound,
ubound, ubound,
sec_cov=sec_cov, sec_cov=sec_cov,
current_position=current_position, current_position=current_position)
**self.running_setting.more_opts)
target_pos['code'] = codes target_pos['code'] = codes
target_pos['trade_date'] = ref_date target_pos['trade_date'] = ref_date
...@@ -239,18 +246,14 @@ class Strategy(object): ...@@ -239,18 +246,14 @@ class Strategy(object):
positions = positions.append(executed_pos) positions = positions.append(executed_pos)
previous_pos = executed_pos previous_pos = executed_pos
positions['benchmark_weight'] = total_data['weight'].values positions['benchmark_weight'] = self.total_data['weight'].values
positions['dx'] = total_data.dx.values positions['dx'] = self.total_data.dx.values
trade_dates = positions.trade_date.unique() trade_dates = positions.trade_date.unique()
ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=trade_dates) ret_df = pd.DataFrame({'returns': rets, 'turn_over': turn_overs, 'leverage': leverags}, index=trade_dates)
index_return = self.engine.fetch_dx_return_index_range(self.running_setting.benchmark, ret_df['benchmark_returns'] = self.index_return['dx']
dates=self.running_setting.dates, ret_df.loc[advanceDateByCalendar('china.sse', ret_df.index[-1], self.freq)] = 0.
horizon=self.running_setting.horizon,
offset=1).set_index('trade_date')
ret_df['benchmark_returns'] = index_return['dx']
ret_df.loc[advanceDateByCalendar('china.sse', ret_df.index[-1], self.running_setting.freq)] = 0.
ret_df = ret_df.shift(1) ret_df = ret_df.shift(1)
ret_df.iloc[0] = 0. ret_df.iloc[0] = 0.
ret_df['excess_return'] = ret_df['returns'] - ret_df['benchmark_returns'] * ret_df['leverage'] ret_df['excess_return'] = ret_df['returns'] - ret_df['benchmark_returns'] * ret_df['leverage']
...@@ -265,20 +268,22 @@ class Strategy(object): ...@@ -265,20 +268,22 @@ class Strategy(object):
sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000 + np.diag(special_risk ** 2) / 10000 sec_cov = risk_exposure @ risk_cov @ risk_exposure.T / 10000 + np.diag(special_risk ** 2) / 10000
return sec_cov return sec_cov
def _calculate_pos(self, er, data, constraints, benchmark_w, lbound, ubound, **kwargs): def _calculate_pos(self, running_setting, er, data, constraints, benchmark_w, lbound, ubound, sec_cov,
current_position):
more_opts = running_setting.more_opts
target_pos, _ = er_portfolio_analysis(er, target_pos, _ = er_portfolio_analysis(er,
industry=data.industry_name.values, industry=data.industry_name.values,
dx_return=None, dx_return=None,
constraints=constraints, constraints=constraints,
detail_analysis=False, detail_analysis=False,
benchmark=benchmark_w, benchmark=benchmark_w,
method=self.running_setting.rebalance_method, method=running_setting.rebalance_method,
lbound=lbound, lbound=lbound,
ubound=ubound, ubound=ubound,
current_position=kwargs.get('current_position'), current_position=current_position,
target_vol=kwargs.get('target_vol'), target_vol=more_opts.get('target_vol'),
cov=kwargs.get('sec_cov'), cov=sec_cov,
turn_over_target=kwargs.get('turn_over_target')) turn_over_target=more_opts.get('turn_over_target'))
return target_pos return target_pos
...@@ -298,6 +303,7 @@ if __name__ == '__main__': ...@@ -298,6 +303,7 @@ if __name__ == '__main__':
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
from matplotlib.pylab import mpl from matplotlib.pylab import mpl
plt.style.use('seaborn-whitegrid') plt.style.use('seaborn-whitegrid')
mpl.rcParams['font.sans-serif'] = ['SimHei'] mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False mpl.rcParams['axes.unicode_minus'] = False
...@@ -309,7 +315,6 @@ if __name__ == '__main__': ...@@ -309,7 +315,6 @@ if __name__ == '__main__':
universe = Universe('zz800') universe = Universe('zz800')
dask_client = Client('10.63.6.176:8786') dask_client = Client('10.63.6.176:8786')
alpha_factors = { alpha_factors = {
'f1': CSQuantiles(LAST('ILLIQUIDITY') * LAST('NegMktValue'), groups='sw1_adj'), 'f1': CSQuantiles(LAST('ILLIQUIDITY') * LAST('NegMktValue'), groups='sw1_adj'),
'f2': CSQuantiles('con_pe', groups='sw1_adj') 'f2': CSQuantiles('con_pe', groups='sw1_adj')
...@@ -329,8 +334,8 @@ if __name__ == '__main__': ...@@ -329,8 +334,8 @@ if __name__ == '__main__':
universe=universe, universe=universe,
batch=1, batch=1,
neutralized_risk=neutralized_risk, neutralized_risk=neutralized_risk,
pre_process=None, #[winsorize_normal, standardize], pre_process=None, # [winsorize_normal, standardize],
post_process=None, #[standardize], post_process=None, # [standardize],
warm_start=1) warm_start=1)
industries = industry_list('sw_adj', 1) industries = industry_list('sw_adj', 1)
...@@ -357,12 +362,7 @@ if __name__ == '__main__': ...@@ -357,12 +362,7 @@ if __name__ == '__main__':
bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val) bounds = create_box_bounds(total_risk_names, b_type, l_val, u_val)
running_setting = RunningSetting(universe, running_setting = RunningSetting(lbound=None,
start_date,
end_date,
freq,
benchmark=906,
lbound=None,
ubound=None, ubound=None,
weights_bandwidth=0.01, weights_bandwidth=0.01,
rebalance_method='risk_neutral', rebalance_method='risk_neutral',
...@@ -370,8 +370,17 @@ if __name__ == '__main__': ...@@ -370,8 +370,17 @@ if __name__ == '__main__':
target_vol=0.05, target_vol=0.05,
turn_over_target=0.4) turn_over_target=0.4)
strategy = Strategy(alpha_model, data_meta, running_setting, dask_client=dask_client) strategy = Strategy(alpha_model,
ret_df, positions = strategy.run() data_meta,
universe=universe,
start_date=start_date,
end_date=end_date,
freq=freq,
benchmark=906,
dask_client=dask_client)
strategy.prepare_backtest_data()
ret_df, positions = strategy.run(running_setting)
ret_df.rename(columns={'excess_return': '超额收益', 'turn_over': '换手率'}, inplace=True) ret_df.rename(columns={'excess_return': '超额收益', 'turn_over': '换手率'}, inplace=True)
ret_df[['超额收益', '换手率']].cumsum().plot(secondary_y='换手率') ret_df[['超额收益', '换手率']].cumsum().plot(secondary_y='换手率')
plt.title("原始ILLIQUIDITY因子") plt.title("原始ILLIQUIDITY因子")
......
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